SOLUTIONS / AI GOVERNANCE
AI governance is how an organization sets and enforces rules for AI use: who can access it, what data it can process, which actions it can take, how its answers are checked, and who is accountable. It covers employee assistants, development tools, and agents running in production.
Cortega brings these controls together through AI Border Gateway, Endpoint Guard, AI Guard, AI Verifier, Cortega Agents, and RedTeam.
THE GOVERNANCE PROBLEM
An employee pastes a document into a chatbot. A coding assistant reads a repository. A production agent calls a customer database. Each workflow needs appropriate access, data protection, model choices, and review—not just a policy document.
BEFORE / AFTER
Each comparison shows an example of a governance gap and how Cortega helps when the relevant controls are configured.
Employees use browser assistants, desktop AI apps, or local tools that never reach a central gateway. IT lacks a reliable view of the apps and data involved.
Use Endpoint Guard on enrolled devices to discover supported AI applications, set observe, police, or block policies, and apply local sensitive-data checks to configured domains.
Delivered by Cortega Endpoint Guard
Explore Shadow AI →Developers configure separate provider keys and models in each coding tool. Source code may reach an unapproved destination, and team policies vary by assistant.
Use Cortega AI Pack for VS Code to configure supported assistants on Windows and macOS. Route their calls through approved models and enforce model locks and team budgets; add Endpoint Guard for device-level Shadow AI controls.
Delivered by Cortega AI Pack + AI Border Gateway
Explore Coding Agents →Teams see a total AI bill but cannot connect usage across assistants to a project, task, or legal matter. Unassigned costs disappear into shared accounts.
Select a matter or project in the extension, or enable the Matter/Project agent to suggest assignments from captured conversations. Review governed request counts, tokens, and model costs by project, keeping unmatched usage visible.
Delivered by Cortega AI Pack + AI FinOps
Explore project cost management →A team adopts a hosted model without deciding whether its data may be processed there. Self-hosting the gateway alone does not determine where the model runs.
Choose approved hosted, local, or privately deployed models for each workload. Apply sensitivity-aware routing and configure inspection, recording, and retention policies for the deployment.
Delivered by AI Border Gateway + Private AI
Explore Private AI →Multiple customers share infrastructure without clearly separated access, policies, or spending limits. Administrators have more access than their responsibilities require.
Use tenant-specific management, policies, model authorization, and budgets alongside role-based access controls, SSO, and administration audit records.
Delivered by Cortega enterprise platform
Explore managed service provider governance →An AI answer violates a writing policy, contains irrelevant material, or is flagged for review, but nobody owns the decision about what happens next.
Define a review rubric and enable scoring, policy compliance, or verification on relevant traffic. Give reviewers access to findings and supporting evidence, and set your organization’s escalation and approval responsibilities.
Delivered by AI Verifier + Cortega Agents
Read about quality and policy analysis →Shared keys make it difficult to identify a caller. Provider credentials spread across services and become hard to rotate.
Use identities and Cortega-issued virtual keys to identify people, applications, and teams. Keep provider credentials encrypted centrally; authenticate supported applications with OIDC/JWT.
Delivered by AI Border Gateway
Explore AI Gateway →A support agent can reach the same tool server as an administrator. Network access alone does not express which tools the agent should use.
Authorize specific teams for specific tools on specific MCP servers, with permission checks on governed calls. For example, allow order lookup without granting customer deletion.
Delivered by AI Border Gateway
Explore tool governance →A request appears harmless in isolation, while earlier messages or a poisoned tool result steer the agent toward an unauthorized action.
Apply guardrails that inspect messages, sessions, and conversations. Configure application threat levels and policy checks alongside tool permissions to constrain what the agent can do.
Delivered by Cortega AI Guard + AI Border Gateway
Explore Agent Security →A customer record or credential is included in a prompt. Sensitive work reaches a model destination that was not approved for that data.
Detect sensitive information and apply configured masking or rejection policies. Route sensitive workloads to approved private models and infrastructure.
Delivered by AI Border Gateway + sensitive-data routing
Explore Private AI →An agent returns an unsupported claim or fabricated citation. Reviewers must decide what to trust without a verification finding.
Enable AI Verifier to check citations, policy adherence, factual relevance, and contextual accuracy using available evidence. Bring findings into the workflow for review.
Delivered by AI Verifier
Read how verification works →An agent repeatedly calls a tool, gives up partway, or produces low-quality work. Request counts and latency alone do not explain the behavior.
Enable Cortega Agents on selected recorded traffic. Agent Trajectory flags loops and wrong-tool use; workload, sentiment, and rubric-based scoring help teams prioritize investigation.
Delivered by Cortega Agents
Read about agent analysis →A model response or tool action cannot be connected to the caller, agent run, or policy decision. Teams reconstruct events from separate logs.
Record per-request identities, model and MCP usage, costs, and findings. Group calls using your application’s conversation or run ID, and retain administration and policy-change audit records.
Delivered by AI Border Gateway + application analytics
Explore application and run analytics →A bill shows model usage but gives little insight into which workflow consumed it. A loop or expensive model choice can keep spending unnoticed.
Set budgets and spending limits for governed requests. Attribute usage to application instances and projects, then use cost-aware routing and workload studies to evaluate eligible alternatives.
Delivered by AI Border Gateway + AI FinOps
Explore AI FinOps →An agent depends on one model endpoint. Errors, timeouts, or degraded service interrupt the workflow.
Configure health-based failover and retries across approved model destinations. Keep fallback choices within your access, privacy, and workload policies.
Delivered by AI Border Gateway
Explore routing and failover →A provider deprecates a model or a team changes a prompt. Earlier test results no longer describe the configuration now serving users.
Study alternatives against your workload and approve model changes through routing. Use RedTeam to compare models and protected configurations, inspect failures, and rerun suites before rollout.
Delivered by Model Intelligence + Cortega RedTeam
Explore AI Benchmarking →APPLY THE CONTROLS
List the employee tools, coding assistants, models, and MCP tools in use, the data they process, and the people responsible for them. Route supported calls through Cortega and enroll devices where local policies are needed.
Configure tool permissions, guardrails, approved model destinations, and budgets. Benchmark the selected configuration and inspect failures before expanding access.
Connect calls to an application run or conversation. Enable the verification and analysis you need, review findings, and retest when the model, prompt, or permissions change.
Identity, tool access, routing, and configured guardrails apply in the governed request path. Cortega Agents analyze selected recorded traffic; a finding from that analysis is not automatically a block on the original action. Configure recording, retention, agent licensing, and review ownership for your deployment.
IN THE CONSOLE
Review agent findings, usage insights, registered MCP servers, and model recommendations in Cortega.
From the Cortega console.
YOUR NEXT STEP
Bring a workflow, its tools, and your data requirements. We’ll show where to apply controls and what evidence to review.